native_api.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGLang Native APIs\n",
    "\n",
    "Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce these following APIs:\n",
    "\n",
    "- `/generate` (text generation model)\n",
    "- `/get_model_info`\n",
    "- `/get_server_info`\n",
    "- `/health`\n",
    "- `/health_generate`\n",
    "- `/flush_cache`\n",
    "- `/update_weights`\n",
    "- `/encode`(embedding model)\n",
    "- `/classify`(reward model)\n",
    "- `/start_expert_distribution_record`\n",
    "- `/stop_expert_distribution_record`\n",
    "- `/dump_expert_distribution_record`\n",
    "\n",
    "We mainly use `requests` to test these APIs in the following examples. You can also use `curl`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch A Server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from sglang.test.test_utils import is_in_ci\n",
    "\n",
    "if is_in_ci():\n",
    "    from patch import launch_server_cmd\n",
    "else:\n",
    "    from sglang.utils import launch_server_cmd\n",
    "\n",
    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
    "\n",
    "\n",
    "server_process, port = launch_server_cmd(\n",
    "    \"python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate (text generation model)\n",
    "Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](./sampling_params.md)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = f\"http://localhost:{port}/generate\"\n",
    "data = {\"text\": \"What is the capital of France?\"}\n",
    "\n",
    "response = requests.post(url, json=data)\n",
    "print_highlight(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Model Info\n",
    "\n",
    "Get the information of the model.\n",
    "\n",
    "- `model_path`: The path/name of the model.\n",
    "- `is_generation`: Whether the model is used as generation model or embedding model.\n",
    "- `tokenizer_path`: The path/name of the tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = f\"http://localhost:{port}/get_model_info\"\n",
    "\n",
    "response = requests.get(url)\n",
    "response_json = response.json()\n",
    "print_highlight(response_json)\n",
    "assert response_json[\"model_path\"] == \"qwen/qwen2.5-0.5b-instruct\"\n",
    "assert response_json[\"is_generation\"] is True\n",
    "assert response_json[\"tokenizer_path\"] == \"qwen/qwen2.5-0.5b-instruct\"\n",
    "assert response_json.keys() == {\"model_path\", \"is_generation\", \"tokenizer_path\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Server Info\n",
    "Gets the server information including CLI arguments, token limits, and memory pool sizes.\n",
    "- Note: `get_server_info` merges the following deprecated endpoints:\n",
    "  - `get_server_args`\n",
    "  - `get_memory_pool_size` \n",
    "  - `get_max_total_num_tokens`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_server_info\n",
    "\n",
    "url = f\"http://localhost:{port}/get_server_info\"\n",
    "\n",
    "response = requests.get(url)\n",
    "print_highlight(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Health Check\n",
    "- `/health`: Check the health of the server.\n",
    "- `/health_generate`: Check the health of the server by generating one token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = f\"http://localhost:{port}/health_generate\"\n",
    "\n",
    "response = requests.get(url)\n",
    "print_highlight(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = f\"http://localhost:{port}/health\"\n",
    "\n",
    "response = requests.get(url)\n",
    "print_highlight(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flush Cache\n",
    "\n",
    "Flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flush cache\n",
    "\n",
    "url = f\"http://localhost:{port}/flush_cache\"\n",
    "\n",
    "response = requests.post(url)\n",
    "print_highlight(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update Weights From Disk\n",
    "\n",
    "Update model weights from disk without restarting the server. Only applicable for models with the same architecture and parameter size.\n",
    "\n",
    "SGLang support `update_weights_from_disk` API for continuous evaluation during training (save checkpoint to disk and update weights from disk).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# successful update with same architecture and size\n",
    "\n",
    "url = f\"http://localhost:{port}/update_weights_from_disk\"\n",
    "data = {\"model_path\": \"qwen/qwen2.5-0.5b-instruct\"}\n",
    "\n",
    "response = requests.post(url, json=data)\n",
    "print_highlight(response.text)\n",
    "assert response.json()[\"success\"] is True\n",
    "assert response.json()[\"message\"] == \"Succeeded to update model weights.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# failed update with different parameter size or wrong name\n",
    "\n",
    "url = f\"http://localhost:{port}/update_weights_from_disk\"\n",
    "data = {\"model_path\": \"qwen/qwen2.5-0.5b-instruct-wrong\"}\n",
    "\n",
    "response = requests.post(url, json=data)\n",
    "response_json = response.json()\n",
    "print_highlight(response_json)\n",
    "assert response_json[\"success\"] is False\n",
    "assert response_json[\"message\"] == (\n",
    "    \"Failed to get weights iterator: \"\n",
    "    \"qwen/qwen2.5-0.5b-instruct-wrong\"\n",
    "    \" (repository not found).\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(server_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encode (embedding model)\n",
    "\n",
    "Encode text into embeddings. Note that this API is only available for [embedding models](openai_api_embeddings.html#openai-apis-embedding) and will raise an error for generation models.\n",
    "Therefore, we launch a new server to server an embedding model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_process, port = launch_server_cmd(\n",
    "    \"\"\"\n",
    "python3 -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-1.5B-instruct \\\n",
    "    --host 0.0.0.0 --is-embedding\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# successful encode for embedding model\n",
    "\n",
    "url = f\"http://localhost:{port}/encode\"\n",
    "data = {\"model\": \"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", \"text\": \"Once upon a time\"}\n",
    "\n",
    "response = requests.post(url, json=data)\n",
    "response_json = response.json()\n",
    "print_highlight(f\"Text embedding (first 10): {response_json['embedding'][:10]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(embedding_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classify (reward model)\n",
    "\n",
    "SGLang Runtime also supports reward models. Here we use a reward model to classify the quality of pairwise generations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(embedding_process)\n",
    "\n",
    "# Note that SGLang now treats embedding models and reward models as the same type of models.\n",
    "# This will be updated in the future.\n",
    "\n",
    "reward_process, port = launch_server_cmd(\n",
    "    \"\"\"\n",
    "python3 -m sglang.launch_server --model-path Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 --host 0.0.0.0 --is-embedding\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "PROMPT = (\n",
    "    \"What is the range of the numeric output of a sigmoid node in a neural network?\"\n",
    ")\n",
    "\n",
    "RESPONSE1 = \"The output of a sigmoid node is bounded between -1 and 1.\"\n",
    "RESPONSE2 = \"The output of a sigmoid node is bounded between 0 and 1.\"\n",
    "\n",
    "CONVS = [\n",
    "    [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE1}],\n",
    "    [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE2}],\n",
    "]\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\")\n",
    "prompts = tokenizer.apply_chat_template(CONVS, tokenize=False)\n",
    "\n",
    "url = f\"http://localhost:{port}/classify\"\n",
    "data = {\"model\": \"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\", \"text\": prompts}\n",
    "\n",
    "responses = requests.post(url, json=data).json()\n",
    "for response in responses:\n",
    "    print_highlight(f\"reward: {response['embedding'][0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(reward_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capture expert selection distribution in MoE models\n",
    "\n",
    "SGLang Runtime supports recording the number of times an expert is selected in a MoE model run for each expert in the model. This is useful when analyzing the throughput of the model and plan for optimization.\n",
    "\n",
    "*Note: We only print out the first 10 lines of the csv below for better readability. Please adjust accordingly if you want to analyze the results more deeply.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expert_record_server_process, port = launch_server_cmd(\n",
    "    \"python3 -m sglang.launch_server --model-path Qwen/Qwen1.5-MoE-A2.7B --host 0.0.0.0\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.post(f\"http://localhost:{port}/start_expert_distribution_record\")\n",
    "print_highlight(response)\n",
    "\n",
    "url = f\"http://localhost:{port}/generate\"\n",
    "data = {\"text\": \"What is the capital of France?\"}\n",
    "\n",
    "response = requests.post(url, json=data)\n",
    "print_highlight(response.json())\n",
    "\n",
    "response = requests.post(f\"http://localhost:{port}/stop_expert_distribution_record\")\n",
    "print_highlight(response)\n",
    "\n",
    "response = requests.post(f\"http://localhost:{port}/dump_expert_distribution_record\")\n",
    "print_highlight(response)\n",
    "\n",
    "import glob\n",
    "\n",
    "output_file = glob.glob(\"expert_distribution_*.csv\")[0]\n",
    "with open(output_file, \"r\") as f:\n",
    "    print_highlight(\"\\n| Layer ID | Expert ID | Count |\")\n",
    "    print_highlight(\"|----------|-----------|--------|\")\n",
    "    next(f)\n",
    "    for i, line in enumerate(f):\n",
    "        if i < 9:\n",
    "            layer_id, expert_id, count = line.strip().split(\",\")\n",
    "            print_highlight(f\"| {layer_id:8} | {expert_id:9} | {count:6} |\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(expert_record_server_process)"
   ]
  }
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